Biomedical subjects

Jeremy van Vlymen

Publications and source records attributed to Jeremy van Vlymen.

6 recordsLinked to original sources

PubMed2006

A study of cardiovascular risk in overweight and obese people in England.

OBJECTIVES: To report current levels of obesity and associated cardiac risk using routinely collected primary care computer data. METHODS: 67 practices took part in an educational intervention to improve computer data quality and care in cardiovascular disease. Data were extracted from 435,102 general practice computer records. 64.3% (229,108/362,861) of people age 15 y and older had a body mass index (BMI) recording or a valid height and weight record that enabled BMI to be derived. Data about cardiovascular disease and risk factors were also extracted. The prevalence of disease and the control of risk factors in the overweight and obese population were compared with those of normal body weight. RESULTS: 56.8% of men and 69.3% of women aged over 15 y had a BMI record. 22% of men and 32.3% of women aged 15 to 24 y were overweight or obese; rising each decade to a peak of 65.6% of men and 57.5% of women aged 55 to 64 y. Thereafter, the proportion who were overweight or obese declined. The prevalence of ischaemic heart disease, diabetes mellitus and hypertension rose with increasing levels of obesity; their prevalence in those who are moderately obese was between two and three times that of the general population. Systolic and diastolic blood pressure, blood glucose even in non-diabetics, cholesterol and triglycerides were all elevated in the overweight and obese population. CONCLUSION: Based on the recorded data over half of men and nearly half of women are overweight or obese. They have increased cardiovascular risk, which is not adequately controlled by current practice.

Adolescent↗

PubMed2006

Routinely-collected general practice data are complex, but with systematic processing can be used for quality improvement and research.

BACKGROUND: UK general practice is computerised, and quality targets based on computer data provide a further incentive to improve data quality. A National Programme for Information Technology is standardising the technical infrastructure and removing some of the barriers to data aggregation. Routinely collected data is an underused resource, yet little has been written about the wide range of factors that need to be taken into account if we are to infer meaning from general practice data. OBJECTIVE: To report the complexity of general practice computer data and factors that need to be taken into account in its processing and interpretation. METHOD: We run clinically focused programmes that provide clinically relevant feedback to clinicians, and overview statistics to localities and researchers. However, to take account of the complexity of these data we have carefully devised a system of process stages and process controls to maintain referential integrity, and improve data quality and error reduction. These are integrated into our design and processing stages. Our systems document the query, reference code set and create unique patient ID. The design stage is followed by appraisal of: data entry issues, how concepts might be represented in clinical systems, coding ambiguities, using surrogates where needed, validation and pilot-ing. The subsequent processing of data includes extraction, migration and integration of data from different sources, cleaning, processing and analysis. RESULTS: Results are presented to illustrate issues with the population denominator, data entry problems, identification of people with unmet needs, and how routine data can be used for real-world testing of pharmaceuticals. CONCLUSIONS: Routinely collected primary care data could contribute more to the process of health improvement; however, those working with these data need to understand fully the complexity of the context within which data entry takes place.

Ambulatory Care Information Systems↗

PubMed2005

A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data.

BACKGROUND: Metadata is data that describes other data or resources. It has a defined number of named elements that convey meaning. Medical data are complex to process. For example, in the Primary Care Data Quality (PCDQ) renal programme, we need to collect over 300 variables because there are so many possible causes of renal disease. These variables are not just single columns of data--all are extracted as code plus date, while others are code-date-value. Metadata has the potential to improve the reliability of processing large datasets. OBJECTIVE: To define unique and unambiguous metadata headings for clinical data and derived variables. METHOD: We defined the look-up tables we would use as a controlled vocabulary to name the core clinical concepts within the metadata. We added six other elements to describe data: (1) the study or audit name; (2) the query used to extract the data; (3) the data collection number; (4) the type of data, including specifying the units; (5) the repeat number (if the variable was extracted more than once); and (6) a processing suffix that defines how the data have been processed. RESULTS: The metadata system has enabled the development of a query library and an analysis syntax library that make data processing and analysis more efficient. Its stability means greater effort can be put into more complex data processing, and some semiautomation of processes. However, the system has had implementation problems. It has been particularly hard to stop clinicians using multiple synonyms for the same variable. CONCLUSIONS: The PCDQ metadata system provides an auditable method of data processing. It is a method that should improve the reliability, validity and efficiency of processing routinely collected clinical data. This paper sets out to demystify our data processing method and makes the PCDQ metadata system available to clinicians and data processors who might wish to adopt it.

Electronic Data Processing↗

PubMed2005

Ensuring the Quality of Aggregated General Practice Data: Lessons from the Primary Care Data Quality Programme (PCDQ).

BACKGROUND: There are large numbers of schemes that collect and aggregate data from primary care computer systems into large databases. These data are then used for market and academic research. How the data is aggregated, cleaned and processed is usually opaque. Making the method transparent allows researchers to compare methods, and users of the output to better understand the strengths and weaknesses of the data.Objectives To define the stages of the process of aggregating, processing and cleaning clinical data from multiple data sources. METHODS: Identify errors in design, collection, staging, integration and analysis. RESULTS: An eight step process defined: (1) Design (2) DATA: entry, (3) Extraction, (4) Migration, (5) Integration, (6) Cleaning, (7) Processing, and (8) Analysis. CONCLUSIONS: This eight step method provides a taxonomy to enable researchers to compare their methods of data process and aggregation.

Computer Systems↗

PubMed2004

Preventing stroke in people with atrial fibrillation: a cross-sectional study.

BACKGROUND: The annual stroke rate in atrial fibrillation is around 5 per cent with increased risk in those with hypertension, diabetes, left ventricular dysfunction and other cardiovascular risk factors. This study set out to identify the patients with atrial fibrillation and modifiable risk factors for stroke. METHOD: Analysis of practice computer data taken from eight general practices (81 811 patients) in the south of England. 944 patients with a diagnosis of atrial fibrillation, of whom 782 (82.8 percent) were aged 65 years and over. RESULTS: The age standardised prevalence of diagnosed atrial fibrillation was 1.23 per cent (1.28 percent for men and 1.18 percent for women). It was much more prevalent in the older population, 8.28 percent and 6.66 percent for males and females over 65, respectively. Cardiovascular co-morbidities were more frequent with increasing age. Blood pressure (BP) was recorded in over 95 per cent of patients with atrial fibrillation though there was scope for improving control; 25 per cent of men and 31 per cent women had a BP over 150/90. Inconsistent recording of ECG and echocardiography made it hard to identify patients with left ventricular dysfunction. Forty six percent of men and 37 percent of women were either being prescribed Warfarin, or had contraindications to its use; of those on Warfarin 75.9 percent have an international normalized ratio in range. Forty four per cent were treated with aspirin. People at high risk of stroke were no more likely to be treated with Warfarin or aspirin than those at moderate risk. CONCLUSIONS: The rate of use of Warfarin remains low, and there is scope for better recording and management of risk factors particularly BP.

Adult↗

PubMed2004

Problems with primary care data quality: osteoporosis as an exemplar.

OBJECTIVE: To report problems implementing a data quality programme in osteoporosis. DESIGN: Analysis of data extracted using Morbidity Information Query and Export Syntax (MIQUEST) from participating general practices' systems and recommendations of practitioners who attended an action research workshop. SETTING: Computerised general practices using different Read code versions to record structured data. PARTICIPANTS: 78 practices predominantly from London and the south east, with representation from north east, north west and south west England. MAIN OUTCOME MEASURES: Patients at risk can be represented in many ways within structured data. Although fracture data exists, it is unclear which are fragility fractures. T-scores, the gold standard for measuring bone density, cannot be extracted using the UK's standard data extraction tool, MIQUEST; instead manual searches had to be implemented. There is a hundredfold variation in data recording levels between practices. Therapy is more frequently recorded than diagnosis. A multidisciplinary forum of experienced practitioners proposed that a limited list of codes should be used. CONCLUSIONS: There is variability in inter-practice data quality. Some clinically important codes are lacking, and there are multiple ways that the same clinical concept can be represented. Different practice computer systems have different versions of Read code, making some data incompatible. Manual searching is still required to find data. Clinicians with an understanding of what data are clinically relevant need to have a stronger voice in the production of codes, and in the creation of recommended lists.

Accidental Falls↗